Selection of Optimal Bands for Hyperspectral Local Feature Descriptor

The use of hyperspectral images (HSIs) and 3-D data has proven to be an efficient combination for numerous applications. A common method of obtaining corresponding data is 3-D reconstruction from HSI, for which the local feature descriptor is vital to the final accuracy. However, redundant bands may hamper the performance of the descriptor, which is similar to the so-called Hughes phenomenon. Band selection (BS) is effective in overcoming such problems. Existing BS methods fail to select the band in a differentiable manner and, thus, cannot be jointly optimized with downstream tasks. In this letter, we propose a novel end-to-end HSI local feature descriptor network (with joint optimal BS) called HyperDesc. It implements a true band selection (TBS) module by turning BS into a CONCRETE random variable-based differentiable sampling operation. The discrete distribution of each selected band can be learned from input HSI with a nonlocal spectral-spatial attention network. Finally, the selected band is sent to a descriptor network to extract the local feature descriptor. The whole network is trained in an end-to-end manner. Experiments are conducted on multiview close-range HSI. The results show that spectral information provided by selected bands can boost the performance of the descriptor.

Automated summary

This letter proposes a novel end-to-end HSI local feature descriptor network called HyperDesc, which implements a true band selection module by turning band selection into a differentiable sampling operation. Experiments demonstrate that the spectral information provided by selected bands can boost the performance of the descriptor.